Case study · NextEra Energy
Bringing fragmented site-evaluation data into one geospatial experience.
Renewable energy developers relied on fragmented GIS tools, environmental data, land-ownership information, and project details to evaluate potential sites. I led the UX strategy and product design for Discover, a unified geospatial experience that brought this information together and explored AI-assisted search and decision support.
Challenge
Developers had to piece together information across multiple tools and data sources before deciding whether a site was worth pursuing.
My role
Experience Architect / Lead UX
Led research, experience strategy, workflow design, interaction design, rapid prototyping, validation, and cross-functional design collaboration.
Impact
Unified
Site evaluation across previously fragmented data sources
The challenge
Every renewable energy project begins with deciding where development effort is worth investing. Developers evaluated potential sites using GIS tools, environmental constraints, land-ownership data, project information, and their own experience.
Because that information lived across multiple tools and systems, developers had to assemble the picture themselves before determining whether a site warranted further investigation.
My role
I ran the research with renewable-energy developers, GIS specialists, and environmental experts to understand how sites were identified and evaluated. I designed the workflows and interactions, built prototypes to test the direction, validated them with users, and worked day to day with Product and Engineering on what was feasible.
Key decisions
01
Site evaluation involved many variables spread across multiple sources. I organized the experience around the factors developers actually weighed — transmission access, land, and environmental conditions — so the information needed to judge whether a site warranted deeper investigation came first.
Research insight
Transmission access was often a gating factor in whether a site was worth pursuing.
02
Viability depends on the relationship between location, transmission infrastructure, land ownership, and environmental constraints. I brought that spatial information together so developers could read conditions in context instead of mentally combining separate tools.
03
Prospecting is inherently spatial, so the map became the primary interaction model rather than a secondary visualization. Developers could explore opportunities geographically, inspect relevant conditions, and move from broad exploration into detailed site investigation without leaving the map.
The solution
The resulting workspace brings site data, geospatial context, constraints, and evaluation criteria together around an interactive map. Developers can explore potential locations, understand relevant conditions, and investigate promising sites without piecing together information across disconnected tools.
01 · Site exploration
Explore potential sites and the information that matters alongside them in a map-centered workspace, with primary search inputs and template actions fixed while filter categories scroll.
02 · Site evaluation
Bring the criteria that define an opportunity — technology, transmission, sentiment, landowner, constructability, and scoring weights — together in one view, and save that configuration as a reusable template rather than rebuilding it each session.
03 · AI-assisted prospecting · Concept
Use conversational assistance to explore site information and support investigation of potential opportunities.
Impact
Unified
Site evaluation across previously fragmented data
Faster screening
Less manual assembly of site information
Better context
Transmission, environmental, land, and project data considered together
AI foundation
Product direction for AI-assisted investigation
We turned fragmented site information into one place to evaluate where to investigate next.